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A new resampling method for sampling designs without replacement: the doubled half bootstrap

Antal, Erika ; Tillé, Yves

In: Computational Statistics, 2014, vol. 29, no. 5, p. 1345-1363

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    Titre
    • A new resampling method for sampling designs without replacement: the doubled half bootstrap
    Auteur
    • Antal, Erika. Swiss Centre of Expertise in the Social Science, Quartier UNIL-Mouline, Bâtiment Géopolis, 1015, Lausanne, Switzerland
    • Tillé, Yves. Faculty of Economics and Business, Institute of Statistics, University of Neuchâtel, Rue de la Pierre-à-Mazel 7, 2000, Neuchâtel, Switzerland
    Type de document
    • Postprint
    Langue
    • Anglais
    Publié dans
    • Computational Statistics, 2014, vol. 29, no. 5, p. 1345-1363. Springer Berlin Heidelberg
    Autre version électronique
    Identifiant OAI-PMH
    • oai:doc.rero.ch:326602
    Summary
    A new and very fast method of bootstrap for sampling without replacement from a finite population is proposed. This method can be used to estimate the variance in sampling with unequal inclusion probabilities and does not require artificial populations or utilization of bootstrap weights. The bootstrap samples are directly selected from the original sample. The bootstrap procedure contains two steps: in the first step, units are selected once with Poisson sampling using the same inclusion probabilities as the original design. In the second step, amongst the non-selected units, half of the units are randomly selected twice. This procedure enables us to efficiently estimate the variance. A set of simulations show the advantages of this new resampling method.